Class AffinityPropagationScikitsLearnNode

Perform Affinity Propagation Clustering of data.
This node has been automatically generated by wrapping the ``sklearn.cluster.affinity_propagation_.AffinityPropagation`` class
from the ``sklearn`` library. The wrapped instance can be accessed
through the ``scikits_alg`` attribute.
Read more in the :ref:`User Guide <affinity_propagation>`.
**Parameters**
damping : float, optional, default: 0.5
Damping factor between 0.5 and 1.
convergence_iter : int, optional, default: 15
Number of iterations with no change in the number
of estimated clusters that stops the convergence.
max_iter : int, optional, default: 200
Maximum number of iterations.
copy : boolean, optional, default: True
Make a copy of input data.
preference : array-like, shape (n_samples,) or float, optional
Preferences for each point - points with larger values of
preferences are more likely to be chosen as exemplars. The number
of exemplars, ie of clusters, is influenced by the input
preferences value. If the preferences are not passed as arguments,
they will be set to the median of the input similarities.
affinity : string, optional, default=``euclidean``
Which affinity to use. At the moment ``precomputed`` and
``euclidean`` are supported. ``euclidean`` uses the
negative squared euclidean distance between points.
verbose : boolean, optional, default: False
Whether to be verbose.
**Attributes**
``cluster_centers_indices_`` : array, shape (n_clusters,)
Indices of cluster centers
``cluster_centers_`` : array, shape (n_clusters, n_features)
Cluster centers (if affinity != ``precomputed``).
``labels_`` : array, shape (n_samples,)
Labels of each point
``affinity_matrix_`` : array, shape (n_samples, n_samples)
Stores the affinity matrix used in ``fit``.
``n_iter_`` : int
Number of iterations taken to converge.
**Notes**
See examples/cluster/plot_affinity_propagation.py for an example.
The algorithmic complexity of affinity propagation is quadratic
in the number of points.
**References**
Brendan J. Frey and Delbert Dueck, "Clustering by Passing Messages
Between Data Points", Science Feb. 2007

Perform Affinity Propagation Clustering of data.
This node has been automatically generated by wrapping the ``sklearn.cluster.affinity_propagation_.AffinityPropagation`` class
from the ``sklearn`` library. The wrapped instance can be accessed
through the ``scikits_alg`` attribute.
Read more in the :ref:`User Guide <affinity_propagation>`.
**Parameters**
damping : float, optional, default: 0.5
Damping factor between 0.5 and 1.
convergence_iter : int, optional, default: 15
Number of iterations with no change in the number
of estimated clusters that stops the convergence.
max_iter : int, optional, default: 200
Maximum number of iterations.
copy : boolean, optional, default: True
Make a copy of input data.
preference : array-like, shape (n_samples,) or float, optional
Preferences for each point - points with larger values of
preferences are more likely to be chosen as exemplars. The number
of exemplars, ie of clusters, is influenced by the input
preferences value. If the preferences are not passed as arguments,
they will be set to the median of the input similarities.
affinity : string, optional, default=``euclidean``
Which affinity to use. At the moment ``precomputed`` and
``euclidean`` are supported. ``euclidean`` uses the
negative squared euclidean distance between points.
verbose : boolean, optional, default: False
Whether to be verbose.
**Attributes**
``cluster_centers_indices_`` : array, shape (n_clusters,)
Indices of cluster centers
``cluster_centers_`` : array, shape (n_clusters, n_features)
Cluster centers (if affinity != ``precomputed``).
``labels_`` : array, shape (n_samples,)
Labels of each point
``affinity_matrix_`` : array, shape (n_samples, n_samples)
Stores the affinity matrix used in ``fit``.
``n_iter_`` : int
Number of iterations taken to converge.
**Notes**
See examples/cluster/plot_affinity_propagation.py for an example.
The algorithmic complexity of affinity propagation is quadratic
in the number of points.
**References**
Brendan J. Frey and Delbert Dueck, "Clustering by Passing Messages
Between Data Points", Science Feb. 2007

_stop_training(self,
**kwargs)

execute(self,
x)

Predict the closest cluster each sample in X belongs to.

This node has been automatically generated by wrapping the sklearn.cluster.affinity_propagation_.AffinityPropagation class
from the sklearn library. The wrapped instance can be accessed
through the scikits_alg attribute.

stop_training(self,
**kwargs)

This node has been automatically generated by wrapping the sklearn.cluster.affinity_propagation_.AffinityPropagation class
from the sklearn library. The wrapped instance can be accessed
through the scikits_alg attribute.